Platt Scaling vs Isotonic Regression for reliability curve

I am learning classifier probability calibrations and have calibrated an eleastic net model using both Platt scaling and isotonic regression. As you can see in the attached image Platt scaling (on the bottom) better approximates the diagonal line compared to isotonic regression (top), however I noticed I am losing information with any predictions where the predicted probability 0.4, I have seen this happen in uncalibrated plots as well. Therefore I am wondering which calibration method I should be using. Furthermore, generally what about the model/data causes the curve to be cut like this and are there things I can do to address this during modeling?

Many thanks!

Update: per suggestion included a plot of the uncalibrated probabilities for comparison purposes.