Social bot detection is essential for maintaining the safety and integrity of online social networks (OSNs). Graph neural networks (GNNs) have emerged as a promising solution. Mainstream GNN-based social bot detection methods learn rich user representations by recursively performing message passing along user-user interaction edges, where users are treated as nodes and their relationships as edges. However, these methods face challenges when detecting advanced bots interacting with genuine accounts. Interaction with real accounts results in the graph structure containing camouflaged and unreliable edges. These unreliable edges interfere with the differentiation between bot and human representations, and the iterative graph encoding process amplifies this unreliability. In this article, we propose a social Bot detection method based on Edge Confidence Evaluation (BECE). Our model incorporates an edge confidence evaluation module that assesses the reliability of the edges and identifies the unreliable edges. Specifically, we design features for edges based on the representation of user nodes and introduce parameterized Gaussian distributions to map the edge embeddings into a latent semantic space. We optimize these embeddings by minimizing Kullback-Leibler (KL) divergence from the standard distribution and evaluate their confidence based on edge representation. Experimental results on three real-world datasets demonstrate that BECE is effective and superior in social bot detection. Additionally, experimental results on six widely used GNN architectures demonstrate that our proposed edge confidence evaluation module can be used as a plug-in to improve detection performance.