The Transformer’s self-attention mechanism has proven effective in medical image segmentation by dynamically weighting global features, yet struggles with local feature correlations. To address these issues, we propose the Efficient Phased Hybrid Attention Decoder (EPHAD), which enhances local feature fusion through integrated channel-spatial attention. While most previous works directly incorporate convolutional modules into encoder-decoder architectures, our EPHAD framework diverges by synergistically integrating attention gates and convolutional attention modules to optimize multi-stage feature aggregation. A residual-enabled framework further accelerates convergence while preserving fine-grained details. Experiments demonstrate that our EPHAD surpasses several recent medical image segmentation network baselines, achieving an 83.21% Dice score. This innovation addresses critical limitations in existing multi-scale feature fusion approaches while maintaining computational efficiency. By synergizing adaptive attention mechanisms with hierarchical feature refinement, EPHAD establishes a robust foundation for developing clinically viable segmentation tools with improved interpretability and reliability.