This paper introduces a murmur detection solution (Team SeaCrying) to the PhysioNet Challenge 2022. The method is based on beat-wise uncertainty learning for heart sounds. The target task is to distinguish the present and absent state for murmur, with an outlier situation indicated as unknown in the challenge. Two uncertainties induced by outlier noise and fuzzy sounds are addressed while beat segmentation and murmur discrimination, respectively. In beat segmentation stage, we employ a confidence branch trained by a frame-level noise contrastive framework to quantify the uncertainty for out-of-distribution episodes. Then we transmit the groups of five effective heart beats to the murmur discriminator and each beat is concatenated by a systole (containing S1 and S2) and a diastole. To alleviate the issue of disability for the model learning unknown sounds, we adopt an uncertainty estimation module on the basis of binary classification for murmur detection. The unknown samples will lead to a highly uncertainty score. As well a cross-beat decision strategy is designed for the same phonocardiogram recording in the final stage. Our proposed method achieved a score of 1082 on hidden test set in the unofficial phase according to the challenge evaluation metric.