In recent years, polyp segmentation plays an important role in the diagnosis and treatment of colorectal cancer. Accurate segmentation of polyps is very challenging due to different sizes, shapes, and unclear boundaries. Making full use of multi-scale contextual information to segment polyps may bring better results. In this paper, we propose an enhanced multi-scale network for accurate polyp segmentation. It is composed of a multi-scale connected baseline (U-Net+++), a multi-scale backbone (Res2Net), three Receptive Field Block (RFB) modules, and four Local Context Attention (LCA) modules. Specifically, the baseline's multi-scale skip connections can aggregate features in both low-level and high-level layers. We have evaluated our model on three publicly available and challenging datasets (EndoScene, CVC-ClinicDB, Kvasir-SEG). Compared with other methods, our model achieves SOTA performance. It is noteworthy that our model is the only network that has achieved over 0.900 mean Dice on EndoScene and CVC-ClinicDB.