Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Despite the widespread adoption of pseudo labeling in semi-supervised learning, existing methods often suffer from noise contamination, which can undermine the robustness of the model. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA, Pancreas-CT and BraTS2019 dataset have demonstrated superior performance over current state-of-the-art techniques, underscoring the efficiency and practicality of our framework. The code is available at https://github.com/ortonwang/SGRS-Net .