Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation

Precise medical image segmentation is crucial for clinical di-agnosis and treatment planning, yet relies heavily on expensive expertannotations. Semi-supervised medical image segmentation (SSMIS) of-fers a cost-effective solution but typically operates under the assump-tion of independent and identically distributed (i.i.d.) data, defaultingto random sampling. While statistically valid at scale, this strategy suf-fers from severe representation bias in low-data regimes, failing to cap-ture the heterogeneous medical data manifold. To address this, we pro-pose a highly data-efficient framework driven by distribution alignment.First, we introduce an offline Distribution-Aware Sample Selection strat-egy. By leveraging Vision Foundation Models (VFMs) and our designedDensity-K-Center algorithm, we explicitly identify representative struc-tural anchors, establishing a more representative labeled domain. Second,to bridge the remaining distribution gap, we propose the Memory-guidedCopy-Paste (MCP) module. Tailored for the inherent class imbalance inmedical scans, MCP leverages a semantic memory mechanism to retrievehistorically consistent priors for cross-domain alignment, encouragingsemantic consistency. Coupled with an easy-to-hard progressive sched-ule, this framework effectively mitigates early-stage pseudo-label noise.Extensive experiments on six diverse 2D and 3D datasets demonstratestrong segmentation performance, particularly in extremely low-labeledscenarios (e.g., 1/16 ratio).