The conventional pavement design approach considers various parameters for the optimization of pavements for the prevailing traffic and environmental conditions. Demands related to technological developments, climate adaptation and infrastructure resilience are expected to influence the way pavements are designed and constructed. For this, better and improved flexible pavement design tools are required. ERAPave performance prediction (PP) which is a mechanistic-empirical (M-E) pavement design tool is currently under development with the goal of addressing the several challenges facing the pavement industry. This paper calibrates the permanent deformation prediction approach in ERAPave PP using pavement performance data from actual field pavements. As traffic volume is observed to have a significant influence on predicted results, separate calibration was performed for medium-to-high-volume and low-volume traffic categories. A global calibration factor is used for this purpose, significantly improving the accuracy of the prediction for both categories. Prediction accuracy can be improved further through the consideration of observed rut depth variability.