A primary challenge in semi-supervised learning (SSL) for segmentation is the confirmation bias from noisy pseudo-labels, which destabilizes training and degrades performance. We propose Inconsistency Masks (IM), a framework that reframes model disagreement not as noise to be averaged away, but as a valuable signal for identifying uncertainty. IM leverages an ensemble of teacher models to generate a mask that explicitly delineates regions where predictions diverge. By filtering these inconsistent areas from input-pseudo-label pairs, our method effectively mitigates the cycle of error propagation common in both continuous and iterative self-training paradigms. Extensive experiments on the Cityscapes benchmark demonstrate IM's effectiveness as a general enhancement framework: when paired with leading approaches like iMAS, U$^2$PL, and UniMatch, our method consistently boosts accuracy, achieving superior benchmarks across ResNet-50 and DINOv2 backbones, and even improving distilled architectures like SegKC. Furthermore, the method's robustness is confirmed in resource-constrained scenarios where pre-trained weights are unavailable. On three additional diverse datasets from medical and underwater domains trained entirely from scratch, IM significantly outperforms standard SSL baselines. Notably, the IM framework is dataset-agnostic, seamlessly handling binary, multi-class, and complex multi-label tasks by operating on discretized predictions. By prioritizing training stability, IM offers a generalizable and robust solution for semi-supervised segmentation, particularly in specialized areas lacking large-scale pre-training data. The full code is available at: https://github.com/MichaelVorndran/InconsistencyMasks

翻译：半监督分割学习中的主要挑战源于噪声伪标签带来的确认偏差，这种偏差会破坏训练稳定性并降低模型性能。本文提出不一致性掩码框架，该框架将模型分歧重新定义为识别不确定性的有效信号，而非需要被平均消除的噪声。IM利用教师模型集合生成显式标注预测分歧区域的掩码，通过从输入-伪标签对中过滤这些不一致区域，本方法有效缓解了连续和迭代自训练范式中常见的误差传播循环。在Cityscapes基准上的大量实验表明，IM作为通用增强框架具有显著效果：当与iMAS、U$^2$PL和UniMatch等领先方法结合时，本方法能持续提升精度，在ResNet-50和DINOv2骨干网络上均取得优越的基准性能，甚至能改进SegKC等蒸馏架构。此外，该方法在缺乏预训练权重的资源受限场景中仍保持鲁棒性。在医学和水下领域三个完全从头训练的数据集上，IM显著优于标准半监督学习基线。值得注意的是，IM框架具有数据集无关性，通过对离散化预测进行操作，可无缝处理二分类、多分类及复杂多标签任务。通过优先保障训练稳定性，IM为半监督分割提供了通用且鲁棒的解决方案，尤其适用于缺乏大规模预训练数据的专业领域。完整代码发布于：https://github.com/MichaelVorndran/InconsistencyMasks