Medical image segmentation is crucial in clinical diagnosis, helping physicians identify and analyze medical conditions. However, this task is often accompanied by challenges like sensitive data, privacy concerns, and expensive annotations. Current research focuses on personalized collaborative training of medical segmentation systems, ignoring that obtaining segmentation annotations is time-consuming and laborious. Achieving a perfect balance between annotation cost and segmentation performance while ensuring local model personalization has become a valuable direction. Therefore, this study introduces a novel Model-Heterogeneous Semi-Supervised Federated (HSSF) Learning framework. It proposes Regularity Condensation and Regularity Fusion to transfer autonomously selective knowledge to ensure the personalization between sites. In addition, to efficiently utilize unlabeled data and reduce the annotation burden, it proposes a Self-Assessment (SA) module and a Reliable Pseudo-Label Generation (RPG) module. The SA module generates self-assessment confidence in real-time based on model performance, and the RPG module generates reliable pseudo-label based on SA confidence. We evaluate our model separately on the Skin Lesion and Polyp Lesion datasets. The results show that our model performs better than other methods characterized by heterogeneity. Moreover, it exhibits highly commendable performance even in homogeneous designs, most notably in region-based metrics. The full range of resources can be readily accessed through the designated repository located at HSSF(github.com) on the platform of GitHub.