In view of increasing pressures of shortened development cycles and desire to save costs, inverse power law scaling has been devised for operative life estimation based on accelerated laboratory test data extrapolation. Derived from the Coffin-Manson's model of fatigue life, this analytical solution however is not capable in addressing probabilistic nature of fatigue. This paper is aimed to provide further insight into the operative life estimation of carbon steel stub axle using parametric models derived from integration of inverse power law stress-life model and probabilistic fatigue life distributions. Maximum likelihood estimation is applied in deriving the parameters of the parametric models and PSN curves are obtained from reliability function of accelerated laboratory test data. PSN curves at operative life as well as any life region can be estimated based on inverse power law acceleration factors. The procedures presented in this study can be applied in estimating the probabilistic fatigue life curves at an accelerated pace resulting in shortened development cycles.