The performance of semi-supervised human pose estimation models relies highly on the quality of pseudo-labels. To refine the quality of pseudo-labels, earlier attempts are mainly ensembling-based, resulting in considerably heavier training workload. In this work, we present a simple approach to estimate the uncertainty of predicted pseudo-labels through a disentangled manner, with unique integration of estimated uncertainty into the training scheme to improve pseudo-label quality. We introduce a pretext task for heatmap regression as the modeling of encoder uncertainty and incorporate heteroscedasticity into unsupervised learning as decoder uncertainty. Experiments show that our method alone achieves comparable performance without data or model ensembling. Meanwhile, our uncertainty estimation technique could further improve model performance when combined with these ensembling-based methods. We also visualize the estimated uncertainty, which further demonstrates the effectiveness of our method.