Convolutional neural networks (CNNs) excel in extracting local features while limited by small receptive fields. Transformers are good at capturing global relationships but are vulnerable to high computational costs at high resolutions. To solve this issue, a Mamba-Enhanced Decoder framework combined with Prototype Consistency learning (MEDPC) is introduced. The Mamba-enhanced auxiliary decoder establishes long-distance dependencies and effectively captures complex shape structure features. In addition, to effectively capture category associations among pixels, we introduce prototype consistency loss to enhance the ability to distinguish category features. With 20% labeled samples, our method achieves 91.1% Dice performance on the LA dataset.