  Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate
the conflict between annotation cost and model performance by adopting sparse
annotation formats (e.g., point, scribble, block, etc.). Typical approaches
attempt to exploit anatomy and topology priors to directly expand sparse
annotations into pseudo-labels. However, due to a lack of attention to the
ambiguous edges in medical images and insufficient exploration of sparse
supervision, existing approaches tend to generate erroneous and overconfident
pseudo proposals in noisy regions, leading to cumulative model error and
performance degradation. In this work, we propose a novel WSS approach, named
ProCNS, encompassing two synergistic modules devised with the principles of
progressive prototype calibration and noise suppression. Specifically, we
design a Prototype-based Regional Spatial Affinity (PRSA) loss to maximize the
pair-wise affinities between spatial and semantic elements, providing our model
of interest with more reliable guidance. The affinities are derived from the
input images and the prototype-refined predictions. Meanwhile, we propose an
Adaptive Noise Perception and Masking (ANPM) module to obtain more enriched and
representative prototype representations, which adaptively identifies and masks
noisy regions within the pseudo proposals, reducing potential erroneous
interference during prototype computation. Furthermore, we generate specialized
soft pseudo-labels for the noisy regions identified by ANPM, providing
supplementary supervision. Extensive experiments on three medical image
segmentation tasks involving different modalities demonstrate that the proposed
framework significantly outperforms representative state-of-the-art methods
