Recent advances in interactive keypoint estimation methods have enhanced keypoint estimation accuracy while aiming to minimize user intervention. However, these methods still depend on user input for error correction, a significant challenge in vertebrae keypoint estimation where densely clustered or overlapping keypoints are common. We introduce a novel two-stage approach that integrates KeyBot that specifically designed to identify and correct significant errors in existing models into existing keypoint estimation frameworks. It is specifically designed to analyze current model predictions and deliver corrective feedback akin to user revision. Trained on simulated error scenarios, KeyBot effectively corrects typical errors in vertebrae keypoint estimation, thereby significantly reducing user workload. Comprehensive quantitative and qualitative evaluations on three public datasets confirm that KeyBot significantly outperforms existing methods, achieving state-of-the-art performance in interactive vertebrae keypoint estimation.