Scribble-supervised methods have emerged to mitigate the prohibitive annotation burden in medical image segmentation. However, the inherent sparsity of these annotations introduces significant ambiguity, which results in noisy pseudo-label propagation and hinders the learning of robust anatomical boundaries. To address this challenge, we propose SDT-Net, a novel dual-teacher, single-student framework designed to maximize supervision quality from these weak signals. Our method features a Dynamic Teacher Switching (DTS) module to adaptively select the most reliable teacher. This selected teacher then guides the student via two synergistic mechanisms: high-confidence pseudo-labels, refined by a Pick Reliable Pixels (PRP) mechanism, and multi-level feature alignment, enforced by a Hierarchical Consistency (HiCo) module. Extensive experiments on the ACDC and MSCMRseg datasets demonstrate that SDT-Net achieves state-of-the-art performance, producing more accurate and anatomically plausible segmentation.

翻译：涂鸦监督方法已出现，旨在减轻医学图像分割中繁重的标注负担。然而，这些标注固有的稀疏性引入了显著的模糊性，导致伪标签传播噪声，并阻碍了对鲁棒解剖边界的学习。为应对这一挑战，我们提出了SDT-Net，一种新颖的双教师-单学生框架，旨在最大化这些弱信号下的监督质量。我们的方法采用动态教师切换模块，自适应地选择最可靠的教师。该选定教师随后通过两种协同机制指导学生：一是通过选取可靠像素机制精炼的高置信度伪标签，二是通过层次一致性模块强化的多层次特征对齐。在ACDC和MSCMRseg数据集上的大量实验表明，SDT-Net实现了最先进的性能，生成了更准确且解剖学上合理的分割结果。