Eddy current models have matured to such a degree that it is now possible to simulate realistic nondestructive inspection (NDI) scenarios. Models have been used in the design and analysis of NDI systems and to a limited extent, model-based inverse methods for Nondestructive Evaluation (NDE). The science base is also being established to quantify the reliability of systems via Model-Assisted Probability of Detection (MAPOD). In realistic situations, it is more accurate to treat the input model variables as random variables rather than deterministic quantities. Typically a Monte-Carlo simulation is conducted to predict the output of a model when the inputs are random variables. This is a reasonable approach as long as computational time is not too long; however, in most applications, introducing a flaw into the model results in extensive computational time ranging from hours to days, prohibiting Monte-Carlo simulations. Even methods such as Latin-Hypercube sampling do not reduce the number of simulations enough for reasonable use. This paper presents the Probabilistic Collocation Method as a non-intrusive alternative to other uncertainty propagation techniques.