Despite remarkable progress in Text-to-SQL semantic parsing, the performance of state-of-the-art parsers are still far from perfect. At the same time, modern deep learning based Text-to-SQL parsers are often over-confident and thus casting doubt on its trustworthiness when used in an interactive setting. In this paper, we propose to train parser-agnostic error detectors for Text-to-SQL semantic parsers. We test our proposed approach with SmBop and show our model could outperform parser-dependent uncertainty measures in simulated interactive evaluations. As a result, when used for answer triggering or interaction trigger in interactive semantic parsing systems, our model could effectively improve the usability of the base parser.