Why does the confidence boundary contain fewer points than expected in R (`ggplot`)? I use a LOESS regression and draw a confidence boundary with a 99% level.
# R code
library(ggplot2)

ggplot(mtcars) + 
  aes(x = mpg, y = disp) + 
  geom_point() +
  geom_smooth(level = 0.99, method = "loess")  


Why are there way fewer than 99% points in the confidence boundary? (7 points out of 32 are outside the confidence boundary).
Is it because it's only an estimate of the confidence boundary, and not the "real" one?

The reason this happens is because a confidence interval quantifies the uncertainty about a fixed parameter (here: the mean at a given point) rather than the sample. This means that as the number of observations grows bigger, the width of the confidence interval actually goes to $0$. In the limit, the proportion of data point inside the confidence interval goes to $0$ (see comments)!
A different way to quantify uncertainty of your data is by a prediction interval that additionally quantifies the uncertainty stemming from sampling variability.
