Accuracy differs between MATLAB and scikit-learn for a decision tree
It is hard to make a direct comparison between a white box implementation (scikit-learn) and a black box implementation (MATLAB). 

One guess they are using different algorithms. scikit-learn uses an optimized version of the CART algorithm. Maybe MATLAB uses  ID3, C4.5, or something else. Another guess two implementations are using different hyperparameters (e.g., different splitting criteria, max depth, minimum node size, ...). 

Since decision trees are white-box models, you can examine their internal structure. Plot both trained trees. See how they each are making the splits and how many splits are being made.