Accurate and real-time polyp segmentation plays a vital role in the early detection of colorectal cancer. However, existing methods often rely on computationally expensive backbones, single attention mechanisms, and suboptimal feature fusion strategies, limiting their practicality in real-world scenarios. In this work, we propose a lightweight yet effective deep learning framework that strikes a balance between precision and efficiency through a carefully designed architecture. Specifically, we adopt a MobileNetV4-based hybrid backbone to extract rich multi-scale features with significantly fewer parameters than conventional backbones, making the model well-suited for resource-constrained clinical settings. To enhance feature representation, we introduce a novel dual-attention guidance mechanism that integrates Efficient Channel Attention (ECA) for channel-wise refinement and Coordinate Attention (COA) for spatial modeling, which is particularly effective at delineating polyp boundaries. Additionally, we design a progressive multi-scale fusion strategy that hierarchically integrates feature maps from deep to shallow layers, preserving spatial details while enhancing contextual understanding. Extensive experiments on five benchmark polyp segmentation datasets demonstrate that our method consistently outperforms state-of-the-art approaches across both quantitative metrics and qualitative visualizations. Comprehensive ablation studies further validate the effectiveness of each component, highlighting the practical viability of our approach for real-time polyp segmentation applications.