Achieving high-precision medical image segmentation while maintaining computational efficiency remains a critical challenge for clinical applications. Existing methods often struggle to balance multi-scale feature fusion, lightweight design and contextual modeling, particularly for complex medical scenes with ambiguous boundaries. To address these limitations, We propose DyMAS-Net, a lightweight framework integrating multi-scale convolution, adaptive dynamic sampling, and dual attention mechanisms. Key innovations include: (i) Hierarchical Multi-Scale Convolution Block (HMCB) combining grouped depthwise convolutions with hybrid attention to capture cross-scale dependencies; (ii) Adaptive Dynamic Sampling Module (ADSM) that dynamically adjusts receptive fields through learnable position offsets and scope prediction, enabling context-aware upsampling with minimal computational overhead; (iii) Dual Attention Fusion Unit (DAFU) integrating channel-spatial attention for global context modeling and depthwise separable gating for local feature refinement. Extensive evaluations across 7 medical image segmentation tasks (breast cancer, thyroid nodules, skin lesions) show DyMAS-Net achieves state-of-the-art performance with an average Dice score of 87.19%, outperforming TransUnet and SwinUnet by 3.02% and 2.77%, respectively. Remarkably, it attains this with only 6.24M parameters and 8.87G FLOPs, 93. 3% fewer parameters than TransUnet. The framework’s efficiency-accuracy balance enables practical deployment in resource-constrained environments, thus promoting health equity.