Heatmap-based methods are mainstream for landmark localization in medical images. However, current heatmap-based methods almost exclusively employ predetermined heatmaps as labels, which cannot fully represent the real location distributions of landmarks to limit performance. Therefore, this study proposes a landmark localization algorithm in medical images based on uncertainty estimation to simultaneously predict the landmarks and their location distributions. The model adopts multi-branch dilated convolution to extract multi-scale context information and employs a self-attention mechanism to enhance important features, thus improving the landmark detection ability while predicting the distributions. Experiments on public datasets show that the proposed method improves the overall landmark detection performance and performs better on most metrics. Additionally, the predicted distributions are consistent with the real annotation distributions.