Medical image segmentation serves as a fundamental and critical component in numerous clinical applications. Recent advancements in interactive segmentation methods have attracted significant attention due to their high accuracy and robustness in complex clinical tasks. However, current deep learning-based interactive segmentation methods exhibit limitations in leveraging user interactions, particularly in interactive encoding design and pixel classification. To address these limitations, this study proposes a hybrid interaction design incorporating “near-center points” and “outer-edge points”, which ensures low interaction costs while accurately capturing user intent. Additionally, the existing geodesic distance encoding method is enhanced by a Gaussian attenuation function to mitigate image noise interference and improve the robustness and accuracy of interaction encoding. Furthermore, a Gaussian process classification method based on a hybrid kernel function is integrated to fully exploit user interaction information during pixel classification, enhancing segmentation accuracy while endowing the model with interpretability. Extensive experiments on five segmentation tasks across four representative subsets of the medical segmentation decathlon (MSD) dataset demonstrate that the proposed method achieves consistently high segmentation accuracy. In particular, for complex tasks such as pancreas tumor and colon image segmentation, this method has significantly higher Dice coefficients and ASSD values than existing methods, showing its strengths in precise segmentation and boundary refinement.