The computational cost of high-fidelity injection molding simulations has been growing in the past years making it more and more challenging to use them for performing analyses such as optimizations or uncertainty quantification. Surrogate modeling offers a cheaper way to realize such studies and has been gaining attention in the field of injection molding simulation. In this work, we propose to compare three surrogate modeling techniques along with two design of experiment methods in their ability to predict the pressure signal at a surface node in a Moldflow simulation by varying process and modeling parameters. A Sobol sensitivity analysis is performed to study the contribution of the varied parameters on the pressure results. In addition, one of the generated models is used along with experimental pressure sensor data to improve the pressure estimation by calibrating the heat transfer coefficients during filling and packing as well as the pressure-dependency coefficient in the Cross-WLF viscosity model. This resulted in major improvements of the pressure predictions for all 27 considered cases in comparison to using the default heat transfer coefficients and viscosity model parameter.