diff --git a/src/spatialdata/models/models.py b/src/spatialdata/models/models.py
index c8001a84..18c285c1 100644
--- a/src/spatialdata/models/models.py
+++ b/src/spatialdata/models/models.py
@@ -1034,25 +1034,30 @@ def _validate_table_annotation_metadata(cls, data: AnnData) -> None:
             raise ValueError(f"`{attr[cls.REGION_KEY_KEY]}` not found in `adata.obs`. Please create the column.")
         if attr[cls.INSTANCE_KEY] not in data.obs:
             raise ValueError(f"`{attr[cls.INSTANCE_KEY]}` not found in `adata.obs`. Please create the column.")
-        if (
-            (dtype := data.obs[attr[cls.INSTANCE_KEY]].dtype)
-            not in [
-                int,
-                np.int16,
-                np.uint16,
-                np.int32,
-                np.uint32,
-                np.int64,
-                np.uint64,
-                "O",
-            ]
-            and not pd.api.types.is_string_dtype(data.obs[attr[cls.INSTANCE_KEY]])
-            or (dtype == "O" and (val_dtype := type(data.obs[attr[cls.INSTANCE_KEY]].iloc[0])) is not str)
-        ):
-            dtype = dtype if dtype != "O" else val_dtype
+        instance_col = data.obs[attr[cls.INSTANCE_KEY]]
+        dtype = instance_col.dtype
+
+        _INT_TYPES = [int, np.int16, np.uint16, np.int32, np.uint32, np.int64, np.uint64]
+
+        def _is_int_or_str_dtype(d: np.dtype) -> bool:
+            return d in _INT_TYPES or isinstance(d, pd.StringDtype)
+
+        # First, check the top-level dtype (covers plain int and StringDtype cases)
+        is_valid = _is_int_or_str_dtype(dtype)
+        # Explicitly handle categorical dtypes by inspecting the categories' dtype, including
+        # object-backed string categories via is_string_dtype on the categories' dtype.
+        if isinstance(dtype, pd.CategoricalDtype):
+            cat_dtype = dtype.categories.dtype
+            is_valid = is_valid or _is_int_or_str_dtype(cat_dtype) or pd.api.types.is_string_dtype(cat_dtype)
+        # the string case is already covered above, the check below covers the case of dtype("O") with string dtype
+        is_valid = is_valid or pd.api.types.is_string_dtype(instance_col)
+
+        if not is_valid:
             raise TypeError(
-                f"Only int, np.int16, np.int32, np.int64, uint equivalents or string allowed as dtype for "
-                f"instance_key column in obs. Dtype found to be {dtype}"
+                f"Only integer (int, np.int16, np.int32, np.int64, and uint equivalents), string "
+                f"(including pandas StringDtype and object dtype with string values), or categorical "
+                f"with integer/string categories allowed as dtype for instance_key column in obs. "
+                f"Dtype found to be {dtype}"
             )
         expected_regions = attr[cls.REGION_KEY] if isinstance(attr[cls.REGION_KEY], list) else [attr[cls.REGION_KEY]]
         found_regions = data.obs[attr[cls.REGION_KEY_KEY]].unique().tolist()
diff --git a/tests/models/test_models.py b/tests/models/test_models.py
index e2087ace..c4ac3347 100644
--- a/tests/models/test_models.py
+++ b/tests/models/test_models.py
@@ -466,6 +466,63 @@ def test_table_model(
         del table.uns[TableModel.ATTRS_KEY]
         _ = TableModel.parse(table)
 
+    @pytest.mark.parametrize(
+        "instance_key_values,instance_key_dtype,should_pass",
+        [
+            # pd.StringDtype: accepted (issue #1062)
+            (["id_0", "id_1", "id_2", "id_3", "id_4"], pd.StringDtype(), True),
+            # object dtype with string values: accepted
+            (["id_0", "id_1", "id_2", "id_3", "id_4"], object, True),
+            # CategoricalDtype with object (string) categories: accepted (issue #1062)
+            (
+                pd.Categorical(["id_0", "id_1", "id_2", "id_3", "id_4"]),
+                None,
+                True,
+            ),
+            # CategoricalDtype with StringDtype categories: accepted (issue #1062)
+            (
+                pd.Categorical(pd.array(["id_0", "id_1", "id_2", "id_3", "id_4"], dtype="string")),
+                None,
+                True,
+            ),
+            # CategoricalDtype with integer categories: accepted
+            (
+                pd.Categorical([0, 1, 2, 3, 4]),
+                None,
+                True,
+            ),
+            # CategoricalDtype with float categories: rejected
+            (
+                pd.Categorical([0.0, 1.0, 2.0, 3.0, 4.0]),
+                None,
+                False,
+            ),
+            # integer dtype: accepted
+            ([0, 1, 2, 3, 4], np.int64, True),
+            # float dtype: rejected
+            ([0.0, 1.0, 2.0, 3.0, 4.0], np.float64, False),
+            # object dtype with non-string values: rejected
+            ([0, 1, 2, 3, 4], object, False),
+        ],
+    )
+    def test_table_instance_key_dtype_validation(self, instance_key_values, instance_key_dtype, should_pass):
+        """Test that _validate_table_annotation_metadata accepts/rejects the correct dtypes for instance_key."""
+        n = 5
+        region = "sample"
+        region_key = "region"
+        obs = pd.DataFrame(index=list(map(str, range(n))))
+        obs[region_key] = pd.Categorical([region] * n)
+        if instance_key_dtype is not None:
+            obs["instance_id"] = pd.array(instance_key_values, dtype=instance_key_dtype)
+        else:
+            obs["instance_id"] = instance_key_values
+        adata = AnnData(RNG.normal(size=(n, 2)), obs=obs)
+        if should_pass:
+            _ = TableModel.parse(adata, region=region, region_key=region_key, instance_key="instance_id")
+        else:
+            with pytest.raises(TypeError, match="allowed as dtype for instance_key column"):
+                TableModel.parse(adata, region=region, region_key=region_key, instance_key="instance_id")
+
     @pytest.mark.parametrize(
         "name",
         [