This paper proposes a novel method for wheat disease image segmentation under limited labeled data. Rather than relying on traditional fine-tuning strategies, we address the challenge through visual parameter-efficient tuning, enabling pre-trained vision models to adapt effectively to the segmentation of wheat diseases with scarce data. Specifically, we freeze the backbone parameters of the segmentation network to mitigate the need for large-scale training samples. However, wheat disease symptoms exhibit distinct properties compared to natural images, such as randomly distributed lesions and interfering symptoms. To address these domain-specific characteristics, we design a disease-aware adapter that enhances the model’s ability to capture lesion-specific patterns and suppress irrelevant features via introducing class guidance, thereby improving disease segmentation accuracy under limited data conditions. Extensive experiments on wheat disease benchmarks demonstrate that the proposed parameter-efficient wheat disease segmenter achieves superior performance compared with state-of-the-art algorithms.