In regression tasks, numerical label noise can distort the true distribution of data and weaken the generalization ability of models. Data filtering is a commonly used approach that can reduce the impact of noise to some extent. However, it is prone to the issue of over-filtering, leading to the loss of effective samples and the shift of data distribution. This study presents a progressive interval correction (PIC) algorithm for regression label noise. The aim is to tackle the problem of sample loss caused by data filtering and effectively reduce the label noise level. First, based on the posterior distribution of the true labels, the validity conditions for label correction are established to ensure a reduction in the label noise level. Then, the labels that meet the validity conditions are corrected using the maximum a posteriori method. Finally, the labels are progressively corrected and optimized by gradually narrowing the range of the credible interval. Experimental results on both benchmark and real-world datasets demonstrate that the PIC algorithm can significantly reduce the noise level of data and effectively enhance the performance of models.