  In weakly supervised medical image segmentation, the absence of structural
priors and the discreteness of class feature distribution present a challenge,
i.e., how to accurately propagate supervision signals from local to global
regions without excessively spreading them to other irrelevant regions? To
address this, we propose a novel weakly supervised medical image segmentation
framework named PCLMix, comprising dynamic mix augmentation, pixel-level
contrastive learning, and consistency regularization strategies. Specifically,
PCLMix is built upon a heterogeneous dual-decoder backbone, addressing the
absence of structural priors through a strategy of dynamic mix augmentation
during training. To handle the discrete distribution of class features, PCLMix
incorporates pixel-level contrastive learning based on prediction uncertainty,
effectively enhancing the model's ability to differentiate inter-class pixel
differences and intra-class consistency. Furthermore, to reinforce segmentation
consistency and robustness, PCLMix employs an auxiliary decoder for dual
consistency regularization. In the inference phase, the auxiliary decoder will
be dropped and no computation complexity is increased. Extensive experiments on
the ACDC dataset demonstrate that PCLMix appropriately propagates local
supervision signals to the global scale, further narrowing the gap between
weakly supervised and fully supervised segmentation methods. Our code is
available at https://github.com/Torpedo2648/PCLMix.
