Abstract: Uncertainty evaluation is a crucial component in the dissemination of measurement values. Following the publication of JJF 1059.2-2012 "Evaluation of Measurement Uncertainty Using the Monte Carlo Method," this approach has been increasingly applied across various metrological disciplines. Using Python, calculation codes were developed employing both the Monte Carlo method and the adaptive Monte Carlo method. Taking the calibration of a 100 MPa oil-medium piston gauge as an example, the uncertainty of the calibrated effective area was evaluated. The impact of uncertainties and probability distribution models of various input quantities on the output uncertainty was analyzed and compared with results from traditional evaluation methods. The findings indicate that the primary source of uncertainty in the calibration of the piston's effective area is the uncertainty introduced by the standard piston's effective area. Results obtained using the Monte Carlo method align with those from traditional evaluations, both yielding a relative expanded uncertainty (k=2) of 32 ppm. The probability density distribution of the calibrated effective area is determined by the probability density distribution of the main influencing factors.