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Rapid and accurate lesion segmentation was essential for assessing disease severity and ensuring precise pesticide application. Deep learning-based semantic segmentation offered the technical foundation necessary for developing high-precision disease detection models. However, the annotation of apple leaf spots was both time-consuming and labor-intensive. To address this issue, a model for apple leaf spot segmentation was proposed based on a lightweight consistency semi-supervised learning framework, using Longdong apples as the research subject. Firstly, following the Mean Teacher semi-supervised learning framework, two lightweight DeepLabV3+ models were utilized to build the lesion semantic segmentation model, which improved its ability to extract feature descriptors from limited annotated data. Secondly, a systematic comparison of 19 consistency regularization methods revealed that the combination of MSE and Huber was more sensitive to subtle image differences and exhibited higher noise resistance, thereby improving the model’s adaptability to small, unevenly distributed, and blurred-edge lesions. Next, a Bayesian algorithm was utilized to optimize six hyperparameters involved in the model, which accelerated convergence speed and enhanced stability. The results demonstrated that the optimized model, using only 30% of the annotated data, achieved a precision of 95.60%, a mean intersection over union (mIoU) of 94.85%, and a mean pixel accuracy (mPA) of 96.50%. These outcomes surpassed those of fully supervised and self-training semi-supervised learning frameworks. The findings offered agricultural practitioners an efficient and reliable tool for disease detection.