Accurate lesion segmentation is crucial for early diagnosis and subsequent treatment of dermatological diseases. Existing neural networks often employ increasingly deep and complex architectures to achieve high segmentation accuracy; however, large parameter counts and high computational costs limit practical deployment. To address these challenges, a lightweight multi-scale channel interaction segmentation network (LMSCI-Net) is proposed. For each input image, a lightweight multi-scale encoding module based on channel separation and convolutional decomposition is designed, augmented by a local-global channel attention mechanism to ensure robust feature extraction while maintaining an efficient encoder. A multi-scale channel interaction enhancement module is then introduced to integrate multi-stage outputs and refine skip connections, providing the decoder with rich and precise detail information. Finally, an adaptive fusion decoding module is developed to progressively restore fine-grained details and produce accurate segmentation masks. The network is trained under a deep supervision regime and evaluated on three public skin-lesion segmentation datasets (ISIC2017, ISIC2018 and ISIC2016) as well as the PH2 dermoscopic image database. Experimental results demonstrate that, compared with the U-Net baseline, LMSCI-Net reduces parameter count and computational complexity by 99.38% and 98.78%, respectively, while maintaining high segmentation accuracy and strong generalization, thus validating its effectiveness and lightweight design.