Evaluating Model Accuracy on a testing data set for a DecisionTreeReegressor Model
Accuracy in ML vocabulary is used mostly for Classification problem i.e. Count of correct prediction out of total.
In a common speaking language, it will mean the predictive correctness of the model esp. on test data.


  My understanding is that it's same as Score which can be calculated simply asregressor.score(X_test, Y_test)


I am assuming that you are using SciKit-Learn, 
score method for DecisionTreeRegressor will return R-square coefficient.Offical Link


  score(self, X, y[, sample_weight]) Return the coefficient of determination R^2 of the prediction.


What should you do -
You should calculate two metrics - R-square and MAE/MSE.
Reason being - for an end-user/business person, MAE would be useful e.g. saying that model's prediction will be ~250$ away from the correct value on an average.

Challenge with MAE/MSE is that it doesn't say if it is good model unless you have an idea of the underlying data. e.g. Creating two models on pricing data of 2 different city - Boston/Tokyo and the MSE is 1000$/$1500. You can't conclude that the former is a better model from this data.
R-square helps here. 
Adjusted R-square (Another regression metrics) - If your feature set is fixed, then you need not check this metrics. It was devised to fix an issue with R-square when the feature set is different for different models.

 Snippet to get RMSE, R-square, Adjusted R-square

#https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics

def reg_metrics(y_test, y_pred, X_train):
    from sklearn.metrics import mean_squared_error, r2_score 

    rmse = np.sqrt(mean_squared_error(y_test,y_pred))
    r2 = r2_score(y_test,y_pred)

    # Scikit-learn doesn't have adjusted r-square, hence custom code
    n = y_pred.shape[0]
    k = X_train.shape[1]
    adj_r_sq = 1 - (1 - r2)*(n-1)/(n-1-k)

    print(rmse, r2, adj_r_sq)


Links to study -
Statistics by Jim
Wikipedia