Aiming at the problem of lacking reliability evaluation and robustness guarantee in existing single-person pose estimation networks′ results, a testing-time-augmentation (TTA) algorithm based on aleatoric uncertainty was proposed. In this TTA algorithm, diverse outputs were firstly obtained by stochastic parallel data augmentation and model inference. Then, the reliability evaluations of those outputs are acquired by calculating their aleatoric uncertainty. Finally, those outputs and their uncertainty were fused according to the reliabilities to obtain a more accurate and robust result as well as its evaluation. Experiments on the MPII dataset show that this algorithm can be applied to any existing single-person pose estimation network in a plug-and-play manner, leading to a more precise and robust result with its uncertainty evaluation.