Precise segmentation of dermatological lesions is vital for clinical diagnostics and therapeutic planning, yet it faces obstacles such as indistinct boundaries, hair interference, measurement tool artifacts, and image noise, which degrade performance. Annotating medical data is costly and time-intensive, and traditional fully supervised methods require extensive labeled data. Semi-supervised learning effectively harnesses limited annotated samples alongside abundant unannotated data to enhance model training. This paper proposes a semi-supervised Mean Teacher neural network model to improve performance using minimal labeled data. Built on the U-Net architecture, it integrates the Mean Teacher framework with consistency regularization and employs Exponential Moving Average (EMA) to enhance learning from unlabeled data. Validated on ISIC2018 and ISIC2016 datasets, the model achieves a Dice coefficient of 89.34% with 30% labeled data, a 4.49% improvement over U-Net alone. Compared to other models, it shows superior results and robust training performance, demonstrating its effectiveness.