The study of model assisted probability of detection in eddy current nondestructive testing requires a large amount of simulation data, while high-precision physical model calculations demand considerable time and are often impractical. The surrogate model is an efficient mathematical model that can replace time-consuming and complex physical models, and is widely used in design optimization problems. CoKriging, a model that fuses high and low-precision data, utilizes a large amount of low-cost and low-precision data, and a small amount of high-cost and high-precision data, which significantly improves the modeling efficiency over Kriging model. It is a very promising surrogate model. This article applies the CoKriging model to the study of the probability of detection aided by the eddy current nondestructive testing model. In the case of detecting groove defects on the surface of a metal plate using a finite-section coil, the CoKriging model is constructed using physical model calculations for some training points. After verifying the accuracy, the CoKriging model can replace the physical model for MAPoD analysis. By comparing the key parameters of MAPoD calculated by the physical model, the accuracy and efficiency of the CoKriging model are verified. The results show that compared with the Kriging model, the CoKriging model only requires fewer sample points to train the model to meet the defined accuracy requirements and in the best-performing example, its construction time is only 7% of that of the Kriging model, greatly improving the efficiency of the MAPoD.