Self-improving Semi-supervised Medical Image Segmentation via Consensus-enhanced Prototype-prompted Segment Anything Model

The scarcity of expert-annotated data has driven the emergence of Semi-Supervised Medical Image Segmentation (SSMIS) as a promising approach. However, due to the absence of ground-truth supervision, current SSMIS methods cannot verify the correctness of self-consistency signals on unlabeled data, resulting in Blind Update. To address this, we propose Self-Improving Consensus-enhanced Prototype-prompted Segment Anything Model (SI-CP2SAM), the first self-improving SSMIS framework, which achieves reliable and accurate segmentation under limited annotation through self-improving mechanism. Specifically, it contains three key novel designs: Attention-guided Kolmogorov-Arnold Prototype Learning (AKaPL), which promotes reliable and debiased representations via Attention-guided Prototype Retrieval (APR) and Kolmogorov-Arnold enhanced Consensus Aggregation (KaCA); Consensus-guided Prototype Decoding (CPD), which yields complementary verifiable signals via prototype-conditioned prompting of SAM; and Anchor-Verified Gated Refinement (AVGR), which maintains a global unlabeled prototype memory and certifies its verifiable evolution via an accept-reject gating mechanism against trustworthy anchors. Extensive experiments on datasets demonstrate the effectiveness of SI-CP2SAM. Code is available at https://github.com/***.